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Automatic detection of suspected sewage discharge from coastal outfalls based on Sentinel-2 imagery / Yuxin Wang in Science of the total environment, vol 853 (December 2022)  

Public [article]  inScience of the total environment > vol 853 (December 2022) . - n° 158374 Titre : Automatic detection of suspected sewage discharge from coastal outfalls based on Sentinel-2 imagery Type de document : Article/Communication Auteurs : Yuxin Wang, Auteur ; Xianqiang He, Auteur ; Yan Bai, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 158374 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse de groupement [Termes IGN] Chine [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification par nuées dynamiques [Termes IGN] couleur de l'océan [Termes IGN] détection automatique [Termes IGN] eau usée [Termes IGN] image Sentinel-MSI [Termes IGN] littoral [Termes IGN] perturbation écologique [Termes IGN] qualité des eaux Résumé : (auteur) Terrestrial pollution has a great impact on the coastal ecological environment, and widely distributed coastal outfalls act as the final gate through which pollutants flow into rivers and oceans. Thus, effectively monitoring the water quality of coastal outfalls is the key to protecting the ecological environment. Satellite remote sensing provides an attractive way to monitor sewage discharge. Selecting the coastal areas of Zhejiang Province, China, as an example, this study proposes an innovative method for automatically detecting suspected sewage discharge from coastal outfalls based on high spatial resolution satellite imageries from Sentinel-2. According to the accumulated in situ observations, we established a training dataset of water spectra covering various optical water types from satellite-retrieved remote sensing reflectance (Rrs). Based on the clustering results from unsupervised classification and different spectral indices, a random forest (RF) classification model was established for the optical water type classification and detection of suspected sewage. The final classification covers 14 optical water types, with type 12 and type 14 corresponding to the high eutrophication water type and suspected sewage water type, respectively. The classification result of model training datasets exhibited high accuracy with only one misclassified sample. This model was evaluated by historical sewage discharge events that were verified by on-site observations and demonstrated that it could successfully recognize sewage discharge from coastal outfalls. In addition, this model has been operationally applied to automatically detect suspected sewage discharge in the coastal area of Zhejiang Province, China, and shows broad application value for coastal pollution supervision, management, and source analysis. Numéro de notice : A2022-859 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.scitotenv.2022.158374 Date de publication en ligne : 28/08/2022 En ligne : https://doi.org/10.1016/j.scitotenv.2022.158374 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102135 [article]